CN110535159A - A kind of method and system of scale energy-accumulating power station running unit fault pre-alarming - Google Patents
A kind of method and system of scale energy-accumulating power station running unit fault pre-alarming Download PDFInfo
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Abstract
本发明公开了一种规模化储能电站运行单元故障预警的方法及系统，其中方法包括：采集储能电池的放电数据作为参考历史数据；采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据；构建重要性的分析模型，分析储能子系统与储能总系统的相关程度，确定每个储能子系统的重要性因数；分析储能单元与储能子系统的相关程度，确定每个储能单元的重要性因数；选取出重要性因数大于预设阈值的储能子系统与储能单元，将储能子系统和储能单元的运行状态参数数据与参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当储能子系统和储能单元预测结果与参考历史数据的偏差大于预设的阈值时，发出故障预警。
The invention discloses a method and system for early warning of faults in operation units of large-scale energy storage power stations, wherein the method includes: collecting discharge data of energy storage batteries as reference historical data; The operating state parameter data of the energy storage unit included in the system; construct an analysis model of importance, analyze the correlation degree between the energy storage subsystem and the total energy storage system, and determine the importance factor of each energy storage subsystem; analyze the energy storage unit The degree of correlation with the energy storage subsystem determines the importance factor of each energy storage unit; select the energy storage subsystem and energy storage unit whose importance factor is greater than the preset threshold, and combine the operation of the energy storage subsystem and energy storage unit State parameter data and reference historical data are analyzed by the long-term and short-term prediction neural network to obtain the prediction result of the next time unit; when the deviation between the prediction result of the energy storage subsystem and the energy storage unit and the reference historical data is greater than the preset threshold, a Failure warning.
Description
技术领域technical field
本发明涉及电力储能技术领域，更具体地，涉及一种规模化储能电站运行单元故障预警的方法及系统。The present invention relates to the technical field of electric power storage, and more specifically, to a method and system for early warning of faults in operation units of large-scale energy storage power stations.
背景技术Background technique
储能技术是新时代能源体系的重要组成部分，同时也是支撑新能源入网的关键性技术。中国的储能产业起步较晚，但发展速度非常快。目前，国内储能技术在示范应用积极探索不同场景、技术、规模和技术路线下的储能商业应用，同时规范相关标准和检测体系。2016～2017年间，我国规划和在建的储能规模近1.6GW，占全球规划和在建规模的34％，我国储能系统投运保持着高速增长。截至2017年底，我国已投运储能项目累计装机规模28.9GW，同比增长19％。电化学储能的累计装机规模为389.8MW，同比增长45％，所占比重为1.3％，较上一年增长0.2个百分点。在各类电化学储能技术中，锂离子电池的累计装机占比最大，比重为58％。Energy storage technology is an important part of the energy system in the new era, and it is also a key technology to support the integration of new energy into the grid. China's energy storage industry started relatively late, but it is developing very fast. At present, domestic energy storage technology is actively exploring the commercial application of energy storage under different scenarios, technologies, scales and technical routes in demonstration applications, and at the same time standardizes relevant standards and testing systems. From 2016 to 2017, my country's planned and under-construction energy storage scale was nearly 1.6GW, accounting for 34% of the global planned and under-construction scale, and my country's energy storage system operation maintained a rapid growth. As of the end of 2017, the cumulative installed capacity of energy storage projects in operation in my country was 28.9GW, a year-on-year increase of 19%. The cumulative installed capacity of electrochemical energy storage was 389.8MW, a year-on-year increase of 45%, accounting for 1.3%, an increase of 0.2 percentage points from the previous year. Among all kinds of electrochemical energy storage technologies, the cumulative installed capacity of lithium-ion batteries accounts for the largest proportion, accounting for 58%.
在这种电化学储能系统越来越广泛的接入电网的背景之下，针对储能系统的检测及故障预警技术也成为了其并行发展的关键技术。储能系统的发电效率随着其容量衰减过程受影响严重，同时在不同的放电效率之下随着时间的推移，其放电能力也有所不同。所以在储能系统运行的过程中，进行时序分析预测并做出故障预警是一项关键的技术问题。Under the background that the electrochemical energy storage system is more and more widely connected to the power grid, the detection and fault warning technology for the energy storage system has also become a key technology for its parallel development. The power generation efficiency of the energy storage system is seriously affected with its capacity fading process, and at the same time, its discharge capacity is also different with the passage of time under different discharge efficiencies. Therefore, during the operation of the energy storage system, it is a key technical issue to perform time series analysis and prediction and make fault warning.
发明内容Contents of the invention
本发明技术方案提供一种规模化储能电站运行单元故障预警的方法及系统，以解决如何对规模化储能电站运行单元故障进行预警的问题。The technical solution of the present invention provides a method and system for early warning of faults in operation units of large-scale energy storage power stations, so as to solve the problem of how to carry out early warning of faults in operation units of large-scale energy storage power stations.
为了解决上述问题，本发明提供了一种规模化储能电站运行单元故障预警的方法，所述方法包括：In order to solve the above problems, the present invention provides a method for early warning of faults in operating units of large-scale energy storage power stations, the method comprising:
采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将所述放电数据作为参考历史数据；Collecting the discharge data during the battery capacity decay process under the normal operation state of the energy storage battery, and using the discharge data as reference historical data;
基于大规模的储能系统，分别采集储能总系统、储能子系统以及所述储能子系统所包含的储能单元的运行状态参数数据；Based on the large-scale energy storage system, respectively collect the operating state parameter data of the energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem;
基于机器学习算法，构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型；分析所述储能子系统与所述储能总系统的相关程度，通过判断每个所述储能子系统的运行状态参数在随机森林中的每棵树上对所述储能总系统的运行状态的影响，确定每个所述储能子系统的重要性因数；分析所述储能单元与所述储能子系统的相关程度，通过判断每个所述储能单元的运行状态参数在随机森林中的每棵树上对所述储能子系统的运行状态的影响，确定每个所述储能单元的重要性因数；Based on a machine learning algorithm, construct an analysis model of the importance among the energy storage system, the energy storage subsystem, and the energy storage unit; analyze the degree of correlation between the energy storage subsystem and the energy storage system , determine the importance factor of each energy storage subsystem by judging the impact of each operating state parameter of the energy storage subsystem on each tree in the random forest on the operating state of the overall energy storage system ; analyze the degree of correlation between the energy storage unit and the energy storage subsystem, and determine the operating state of the energy storage subsystem on each tree in the random forest by judging the operating state parameters of each of the energy storage units , determining the importance factor of each of the energy storage units;
分别选取出所述储能子系统与所述储能单元的重要性因数大于预设阈值的所述储能子系统与所述储能单元，将所述储能子系统和所述储能单元的运行状态参数数据与所述参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当所述储能子系统和所述储能单元预测结果与所述参考历史数据的偏差大于预设的阈值时，发出故障预警。Selecting the energy storage subsystem and the energy storage unit whose importance factors of the energy storage subsystem and the energy storage unit are greater than a preset threshold, and combining the energy storage subsystem and the energy storage unit The operating state parameter data and the reference historical data are analyzed using the long-short time prediction neural network to obtain the prediction result of the next time unit; when the prediction results of the energy storage subsystem and the energy storage unit are compared with the reference historical data When the deviation is greater than the preset threshold, a fault warning is issued.
优选地，所述采集储能电池正常运行状态下电池容量衰减过程中的放电数据，所述放电数据包括：实时放电功率、放电电压、以及荷电状态；Preferably, the collection of discharge data during the battery capacity decay process under the normal operation state of the energy storage battery, the discharge data includes: real-time discharge power, discharge voltage, and state of charge;
当采集到的放电数据的数据量超过运行状态放电数据的额定数据量时，以实际工况的放电数据代替运行状态额定的放电数据作为新的运行状态放电数据；When the data volume of the collected discharge data exceeds the rated data volume of the discharge data in the operating state, the discharge data of the actual working condition replaces the rated discharge data of the operating state as the new discharge data of the operating state;
设置放电数据的预设数据量，当采集到的放电数据的数据量超过所述预设数据量时，则用新的放电数据代替历史放电数据。A preset data volume of the discharge data is set, and when the data volume of the collected discharge data exceeds the preset data volume, new discharge data is used to replace the historical discharge data.
优选地，所述基于大规模的储能系统，分别采集储能总系统、储能子系统以及所述储能子系统所包含的储能单元的运行状态参数数据包括：Preferably, based on the large-scale energy storage system, respectively collecting the operating state parameter data of the total energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem includes:
采集所述储能总系统的总体运行功率、运行总电压、以及总体荷电状态；采集所述储能子系统的运行功率、运行电压、以及荷电状态；采集所述储能单元的运行功率、运行电压、以及荷电状态；Collect the overall operating power, operating total voltage, and overall state of charge of the energy storage total system; collect the operating power, operating voltage, and state of charge of the energy storage subsystem; collect the operating power of the energy storage unit , operating voltage, and state of charge;
将所述储能总系统、所述储能子系统和所述储能单元的同类参数的数据进行分类，生成储能系统运行功率数据库、储能系统运行电压数据库和储能系统运行荷电状态数据库。Classify the data of similar parameters of the energy storage system, the energy storage subsystem, and the energy storage unit to generate the energy storage system operating power database, energy storage system operating voltage database, and energy storage system operating state of charge database.
优选地，所述基于机器学习算法，构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型，还包括：Preferably, the constructing an analysis model of the importance among the overall energy storage system, the energy storage subsystem and the energy storage unit based on a machine learning algorithm further includes:
基于机器学习算法，应用随机森林算法构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型。Based on a machine learning algorithm, a random forest algorithm is used to construct an analysis model of the importance among the energy storage system, the energy storage subsystem, and the energy storage units.
优选地，所述采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将所述放电数据作为参考历史数据，还包括：Preferably, the collecting discharge data during the battery capacity decay process under the normal operation state of the energy storage battery, using the discharge data as reference historical data, further includes:
电池容量按照不同速度衰减速度进行衰减。The battery capacity decays at different speeds.
基于本发明的另一方面，提供一种规模化储能电站运行单元故障预警的系统，所述系统包括：Based on another aspect of the present invention, a system for early warning of faults in operation units of large-scale energy storage power stations is provided, the system includes:
第一采集单元，用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将所述放电数据作为参考历史数据；The first collection unit is used to collect the discharge data during the battery capacity decay process under the normal operation state of the energy storage battery, and use the discharge data as reference historical data;
第二采集单元，用于基于大规模的储能系统，分别采集储能总系统、储能子系统以及所述储能子系统所包含的储能单元的运行状态参数数据；The second collection unit is used to separately collect the operating state parameter data of the total energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem based on the large-scale energy storage system;
构建单元，用于基于机器学习算法，构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型；分析所述储能子系统与所述储能总系统的相关程度，通过判断每个所述储能子系统的运行状态参数在随机森林中的每棵树上对所述储能总系统的运行状态的影响，确定每个所述储能子系统的重要性因数；分析所述储能单元与所述储能子系统的相关程度，通过判断每个所述储能单元的运行状态参数在随机森林中的每棵树上对所述储能子系统的运行状态的影响，确定每个所述储能单元的重要性因数；The construction unit is used to construct an analysis model of the importance among the overall energy storage system, the energy storage subsystem and the energy storage unit based on a machine learning algorithm; analyze the relationship between the energy storage subsystem and the energy storage The degree of correlation of the overall system, by judging the impact of the operating state parameters of each of the energy storage subsystems on each tree in the random forest on the operating state of the overall energy storage system, to determine each of the energy storage subsystems The importance factor of the system; analyze the degree of correlation between the energy storage unit and the energy storage subsystem, and evaluate the energy storage unit on each tree in the random forest by judging the operating state parameters of each energy storage unit the influence of the operating state of the subsystems, determining the importance factor of each of said energy storage units;
预警单元，用于分别选取出所述储能子系统与所述储能单元的重要性因数大于预设阈值的所述储能子系统与所述储能单元，将所述储能子系统和所述储能单元的运行状态参数数据与所述参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当所述储能子系统和所述储能单元预测结果与所述参考历史数据的偏差大于预设的阈值时，发出故障预警。The early warning unit is used to select the energy storage subsystem and the energy storage unit whose importance factors of the energy storage subsystem and the energy storage unit are greater than a preset threshold, and select the energy storage subsystem and the energy storage unit The operating state parameter data of the energy storage unit and the reference historical data are analyzed using a long-term and short-term prediction neural network to obtain the prediction result of the next time unit; when the prediction result of the energy storage subsystem and the energy storage unit is consistent with When the deviation of the reference historical data is greater than a preset threshold, a fault warning is issued.
优选地，所述第一采集单元用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，所述放电数据包括：实时放电功率、放电电压、以及荷电状态；Preferably, the first collection unit is used to collect discharge data in the process of battery capacity decay under normal operation of the energy storage battery, and the discharge data includes: real-time discharge power, discharge voltage, and state of charge;
当采集到的放电数据的数据量超过运行状态放电数据的额定数据量时，以实际工况的放电数据代替运行状态额定的放电数据作为新的运行状态放电数据；When the data volume of the collected discharge data exceeds the rated data volume of the discharge data in the operating state, the discharge data of the actual working condition replaces the rated discharge data of the operating state as the new discharge data of the operating state;
设置放电数据的预设数据量，当采集到的放电数据的数据量超过所述预设数据量时，则用新的放电数据代替历史放电数据。A preset data volume of the discharge data is set, and when the data volume of the collected discharge data exceeds the preset data volume, new discharge data is used to replace the historical discharge data.
优选地，所述第二采集单元用于基于大规模的储能系统，分别采集储能总系统、储能子系统以及所述储能子系统所包含的储能单元的运行状态参数数据包括：Preferably, the second collection unit is used to separately collect operating state parameter data of the total energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem based on a large-scale energy storage system, including:
采集所述储能总系统的总体运行功率、运行总电压、以及总体荷电状态；采集所述储能子系统的运行功率、运行电压、以及荷电状态；采集所述储能单元的运行功率、运行电压、以及荷电状态；Collect the overall operating power, operating total voltage, and overall state of charge of the energy storage total system; collect the operating power, operating voltage, and state of charge of the energy storage subsystem; collect the operating power of the energy storage unit , operating voltage, and state of charge;
将所述储能总系统、所述储能子系统和所述储能单元的同类参数的数据进行分类，生成储能系统运行功率数据库、储能系统运行电压数据库和储能系统运行荷电状态数据库。Classify the data of similar parameters of the energy storage system, the energy storage subsystem, and the energy storage unit to generate the energy storage system operating power database, energy storage system operating voltage database, and energy storage system operating state of charge database.
优选地，所述构建单元用于基于机器学习算法，构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型，还包括：Preferably, the construction unit is used to construct an analysis model of the importance among the overall energy storage system, the energy storage subsystem, and the energy storage unit based on a machine learning algorithm, and further includes:
基于机器学习算法，应用随机森林算法构建所述储能总系统、所述储能子系统以及所述储能单元之间重要性的分析模型。Based on a machine learning algorithm, a random forest algorithm is used to construct an analysis model of the importance among the energy storage system, the energy storage subsystem, and the energy storage units.
优选地，所述第二采集单元用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将所述放电数据作为参考历史数据，还包括：Preferably, the second collection unit is used to collect discharge data during the battery capacity decay process of the energy storage battery in a normal operating state, using the discharge data as reference historical data, and further comprising:
电池容量按照不同速度衰减速度进行衰减。The battery capacity decays at different speeds.
本发明技术方案提供一种规模化储能电站运行单元故障预警的方法及系统，其中方法包括：采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将放电数据作为参考历史数据；基于大规模的储能系统，分别采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据；基于机器学习算法，构建储能总系统、储能子系统以及储能单元之间重要性的分析模型；分析储能子系统与储能总系统的相关程度，通过判断每个储能子系统的运行状态参数在随机森林中的每棵树上对储能总系统的运行状态的影响，确定每个储能子系统的重要性因数；分析储能单元与储能子系统的相关程度，通过判断每个储能单元的运行状态参数在随机森林中的每棵树上对储能子系统的运行状态的影响，确定每个储能单元的重要性因数；分别选取出储能子系统与储能单元的重要性因数大于预设阈值的储能子系统与储能单元，将储能子系统和储能单元的运行状态参数数据与参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当储能子系统和储能单元预测结果与参考历史数据的偏差大于预设的阈值时，发出故障预警。本发明技术方案针对大规模储能系统中的故障分析，考虑储能系统受储能单元的短板效应影响明显，当储能系统中某一储能单元发生故障时会导致整体储能系统运行参数发生较大变化。在进行故障检测时，不针对物理因素进行故障排查，以储能系统运行状态参数为主要参考，在数据可视化的情况下直接判别故障发生的单元进行故障预警工作。The technical solution of the present invention provides a method and system for early warning of faults in the operation unit of a large-scale energy storage power station, wherein the method includes: collecting discharge data during the battery capacity decay process of the energy storage battery in a normal operating state, and using the discharge data as reference historical data; Based on the large-scale energy storage system, the operating state parameter data of the energy storage system, the energy storage subsystem and the energy storage units contained in the energy storage subsystem are collected respectively; based on the machine learning algorithm, the energy storage system, the energy storage subsystem The analysis model of the importance between the system and the energy storage unit; analyze the degree of correlation between the energy storage subsystem and the total energy storage system, by judging the operating state parameters of each energy storage subsystem on each tree in the random forest. Determine the importance factor of each energy storage subsystem based on the influence of the operating state of the total energy system; analyze the degree of correlation between the energy storage unit and the energy storage subsystem, and determine the value of each energy storage unit’s operating state parameters in the random forest The influence of each tree on the operating state of the energy storage subsystem determines the importance factor of each energy storage unit; respectively selects the energy storage subsystem and the energy storage subsystem whose importance factor is greater than the preset threshold With the energy storage unit, the operating state parameter data of the energy storage subsystem and the energy storage unit and the reference historical data are analyzed using the long-term and short-term prediction neural network to obtain the prediction result of the next time unit; when the energy storage subsystem and the energy storage unit When the deviation between the prediction result and the reference historical data is greater than the preset threshold, a fault warning is issued. The technical solution of the present invention is aimed at fault analysis in a large-scale energy storage system, considering that the energy storage system is significantly affected by the short board effect of the energy storage unit, and when a certain energy storage unit in the energy storage system fails, it will lead to the operation of the entire energy storage system Parameters have changed significantly. When performing fault detection, physical factors are not used for troubleshooting, and the operating state parameters of the energy storage system are used as the main reference. In the case of data visualization, the unit where the fault occurs is directly identified for fault warning.
附图说明Description of drawings
通过参考下面的附图，可以更为完整地理解本发明的示例性实施方式：A more complete understanding of the exemplary embodiments of the present invention can be had by referring to the following drawings:
图1为根据本发明优选实施方式的规模化储能电站运行单元故障预警的方法流程图；Fig. 1 is a flow chart of a method for early warning of a fault in an operating unit of a large-scale energy storage power station according to a preferred embodiment of the present invention;
图2为根据本发明优选实施方式的规模化储能电站运行单元故障预警的方法流程图；以及Fig. 2 is a flow chart of a method for early warning of faults in operating units of large-scale energy storage power plants according to a preferred embodiment of the present invention; and
图3为根据本发明优选实施方式的规模化储能电站运行单元故障预警的系统结构图。Fig. 3 is a system structure diagram of a fault warning system for operating units of a large-scale energy storage power station according to a preferred embodiment of the present invention.
具体实施方式Detailed ways
现在参考附图介绍本发明的示例性实施方式，然而，本发明可以用许多不同的形式来实施，并且不局限于此处描述的实施例，提供这些实施例是为了详尽地且完全地公开本发明，并且向所属技术领域的技术人员充分传达本发明的范围。对于表示在附图中的示例性实施方式中的术语并不是对本发明的限定。在附图中，相同的单元/元件使用相同的附图标记。Exemplary embodiments of the present invention will now be described with reference to the drawings; however, the present invention may be embodied in many different forms and are not limited to the embodiments described herein, which are provided for the purpose of exhaustively and completely disclosing the present invention. invention and fully convey the scope of the invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings do not limit the present invention. In the figures, the same units/elements are given the same reference numerals.
除非另有说明，此处使用的术语(包括科技术语)对所属技术领域的技术人员具有通常的理解含义。另外，可以理解的是，以通常使用的词典限定的术语，应当被理解为与其相关领域的语境具有一致的含义，而不应该被理解为理想化的或过于正式的意义。Unless otherwise specified, the terms (including scientific and technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it can be understood that terms defined by commonly used dictionaries should be understood to have consistent meanings in the context of their related fields, and should not be understood as idealized or overly formal meanings.
图1为根据本发明优选实施方式的规模化储能电站运行单元故障预警的方法流程图。本申请实施方式以储能电池的实验室数据及现场工况数据为基础，在运行过程中通过不断地数据更新适应不同情况下的不同需求，避免预先设定相关数据及标准带来的错误估计，根据实际工况需求设定故障预警标准。同时针对不同时间尺度的数据采集设定不同的数据输入规模。本申请在时序预测的过程中，采用长短时神经网络算法当发生较大的波动时会导致整体运行过程只能保证时许跟踪趋势的稳定，而无法在一定误差内完全时序跟踪系统。本申请在整体发生较大抖动时以加权的方法平衡抖动产生的误差，在一定误差的误差标准下，当总体预测趋势仍能与实际工况状态相符则以相应权重缩小实际值与预测值之间的差距。如图1所示，一种规模化储能电站运行单元故障预警的方法，方法包括：Fig. 1 is a flow chart of a method for early warning of faults in operating units of large-scale energy storage power plants according to a preferred embodiment of the present invention. The implementation mode of this application is based on the laboratory data and on-site working condition data of the energy storage battery, and adapts to different needs in different situations through continuous data updating during the operation process, avoiding miscalculation caused by pre-setting relevant data and standards , and set fault warning standards according to actual working conditions. At the same time, different data input scales are set for data acquisition of different time scales. In the process of time series prediction, this application adopts the long-short time neural network algorithm. When large fluctuations occur, the overall operation process can only ensure the stability of the time tracking trend, but cannot completely track the system within a certain error. This application uses a weighted method to balance the error caused by the jitter when the overall jitter occurs. Under a certain error standard, when the overall forecast trend can still be consistent with the actual working condition, the corresponding weight is used to reduce the actual value and the predicted value. gap between. As shown in Figure 1, a method for early warning of faults in operating units of a large-scale energy storage power station, the method includes:
优选地，在步骤101：采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将放电数据作为参考历史数据。优选地，采集储能电池正常运行状态下电池容量衰减过程中的放电数据，放电数据包括：实时放电功率、放电电压、以及荷电状态；当采集到的放电数据的数据量超过运行状态放电数据的额定数据量时，以实际工况的放电数据代替运行状态额定的放电数据作为新的运行状态放电数据；设置放电数据的预设数据量，当采集到的放电数据的数据量超过预设数据量时，则用新的放电数据代替历史放电数据。优选地，采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将放电数据作为参考历史数据，还包括：电池容量按照不同速度衰减速度进行衰减。Preferably, in step 101: collecting discharge data during the battery capacity decay process of the energy storage battery in a normal operating state, and using the discharge data as reference historical data. Preferably, the discharge data during the battery capacity decay process is collected under the normal operation state of the energy storage battery. The discharge data includes: real-time discharge power, discharge voltage, and state of charge; when the data volume of the collected discharge data exceeds the discharge data of the operating state When the rated data volume of the actual working condition is used, the discharge data of the actual working condition is used instead of the rated discharge data of the running state as the new discharge data of the running state; the preset data volume of the discharge data is set, and when the data volume of the collected discharge data exceeds the preset data When the amount is large, the new discharge data is used to replace the historical discharge data. Preferably, collecting the discharge data during the battery capacity decay process in the normal operating state of the energy storage battery, using the discharge data as reference historical data, further includes: the battery capacity decays at different speeds.
本申请根据储能系统所用电池的实验室及出厂状态运行参数作为历史数据，以实际工况需求下的多种不同放电速率(例如放电速率可以为：1C、1.5C、2.5C，本申请可按实际需求进行选择)进行储能电池正常运行状态下随容量衰减的运行状态参数额定数据集，包括放电过程中的实时放电功率(Punit)、放电电压(Vunit)、以及荷电状态(SOCunit)；This application uses the laboratory and factory state operating parameters of the battery used in the energy storage system as historical data, and uses a variety of different discharge rates under actual working conditions (for example, the discharge rate can be: 1C, 1.5C, 2.5C, this application can be Select according to actual needs) to carry out the rated data set of operating state parameters with capacity decay under the normal operating state of the energy storage battery, including real-time discharging power (P unit ), discharging voltage (V unit ), and state of charge ( SOC unit );
本申请以运行状态参数额定数据集为标准，采用机器学习中的多项式回归算法，绘制储能电池随容量衰减的运行状态趋势曲线，并输出其对应的权重中参数及截距参数(w1,...wn,b)；This application takes the rated data set of operating state parameters as the standard, adopts the polynomial regression algorithm in machine learning, draws the operating state trend curve of the energy storage battery with capacity decay, and outputs its corresponding weight parameters and intercept parameters (w 1 , ... w n ,b);
本申请在实际工况运行的过程中，当采集到的系统正常运行数据集的数据量超过运行状态额定数据集的数据量时，以实际工况数据集代替运行状态额定数据集作为新的运行状态额定数据集，同时设定一个额定容量，每当新的数据容量超过这个设定容量，则以新的数据集代替历史数据集。During the operation of the application under actual working conditions, when the data volume of the normal operation data set of the collected system exceeds the data volume of the rated data set of the operating state, the actual working condition data set replaces the rated data set of the operating state as the new operation State rated data set, and set a rated capacity at the same time, whenever the new data capacity exceeds the set capacity, the new data set will replace the historical data set.
优选地，在步骤102：基于大规模的储能系统，分别采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据。优选地，基于大规模的储能系统，分别采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据包括：采集储能总系统的总体运行功率、运行总电压、以及总体荷电状态；采集储能子系统的运行功率、运行电压、以及荷电状态；采集储能单元的运行功率、运行电压、以及荷电状态；将储能总系统、储能子系统和储能单元的同类参数的数据进行分类，生成储能系统运行功率数据库、储能系统运行电压数据库和储能系统运行荷电状态数据库。Preferably, in step 102: Based on the large-scale energy storage system, the operating state parameter data of the total energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem are respectively collected. Preferably, based on a large-scale energy storage system, respectively collecting the operating state parameter data of the energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem includes: collecting the overall operating power of the energy storage system, The total operating voltage and the overall state of charge; collect the operating power, operating voltage, and state of charge of the energy storage subsystem; collect the operating power, operating voltage, and state of charge of the energy storage unit; Classify the data of similar parameters of the energy subsystem and energy storage unit to generate the energy storage system operating power database, energy storage system operating voltage database and energy storage system operating state of charge database.
本申请针对规模化电化学储能电站，分别形成储能总系统(PCS)、储能子系统(PCSn)、及储能子系统所包含的储能单元(BMSnn)的运行状态参数数据库。本申请针对规模化电化学储能电站形成运行状态参数数据库的过程为：This application aims at the large-scale electrochemical energy storage power station, respectively forming the operating state parameter database of the total energy storage system (PCS), the energy storage subsystem (PCSn), and the energy storage unit (BMSnn) included in the energy storage subsystem. The process of this application to form the operating state parameter database for large-scale electrochemical energy storage power plants is as follows:
本申请针对规模化电化学储能电站的运行状态参数，形成多级数据采集存储系统，分别形成储能总系统(PCS)、储能子系统(PCSn)、及储能子系统所包含的储能单元(BMSnn)的运行状态参数数据库。This application aims to form a multi-level data acquisition and storage system for the operating state parameters of large-scale electrochemical energy storage power stations, and respectively form the total energy storage system (PCS), the energy storage subsystem (PCSn), and the storage systems included in the energy storage subsystem. Operational state parameter database of energy unit (BMSnn).
本申请在采集储能系统数据的过程中，分别采集总体大规模储能系统运行过程中的状态参数，比如总体运行功率(Ptotal)、运行总电压(Vtotal)、总体荷电状态(SOCtotal)等，以及运行每一单体储能系统的运行功率(Pn)、运行电压(Vn)、荷电状态(SOCn)，以及下属各个单元的运行状态功率(Pnn)、运行电压(Vnn)、荷电状态(SOCn)，并将对应的储能总系统，储能子系统，相应的储能单元的同参数数据分类进而形成，储能系统运行功率数据库(DataP)、储能系统运行电压数据库(DataV)、储能系统运行荷电状态数据库(Datasoc)。In the process of collecting energy storage system data, the present application separately collects state parameters during the operation of the overall large-scale energy storage system, such as overall operating power (P total ), operating total voltage (V total ), overall state of charge (SOC total ), etc., as well as the operating power (P n ), operating voltage (Vn), and state of charge (SOCn) of each individual energy storage system, as well as the operating state power (Pnn) and operating voltage (V nn ), state of charge (SOC n ), and classify and form the same parameter data of the corresponding energy storage system, energy storage subsystem, and corresponding energy storage unit . Energy system operating voltage database (Data V ), energy storage system operating state of charge database (Data soc ).
优选地，在步骤103：基于机器学习算法，构建储能总系统、储能子系统以及储能单元之间重要性的分析模型；分析储能子系统与储能总系统的相关程度，通过判断每个储能子系统的运行状态参数在随机森林中的每棵树上对储能总系统的运行状态的影响，确定每个储能子系统的重要性因数；分析储能单元与储能子系统的相关程度，通过判断每个储能单元的运行状态参数在随机森林中的每棵树上对储能子系统的运行状态的影响，确定每个储能单元的重要性因数。Preferably, in step 103: based on a machine learning algorithm, construct an analysis model for the importance of the total energy storage system, the energy storage subsystem, and the energy storage unit; analyze the degree of correlation between the energy storage subsystem and the total energy storage system, and judge The impact of the operating state parameters of each energy storage subsystem on the operating state of the total energy storage system on each tree in the random forest, determine the importance factor of each energy storage subsystem; analyze the energy storage unit and energy storage sub-system The degree of correlation of the system determines the importance factor of each energy storage unit by judging the impact of the operating state parameters of each energy storage unit on each tree in the random forest on the operating state of the energy storage subsystem.
优选地，在步骤104：分别选取出储能子系统与储能单元的重要性因数大于预设阈值的储能子系统与储能单元，将储能子系统和储能单元的运行状态参数数据与参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当储能子系统和储能单元预测结果与参考历史数据的偏差大于预设的阈值时，发出故障预警。Preferably, in step 104: respectively select the energy storage subsystem and the energy storage unit whose importance factor is greater than the preset threshold, and store the operating state parameter data of the energy storage subsystem and the energy storage unit The long-short-time prediction neural network is used to analyze the reference historical data to obtain the prediction result of the next time unit; when the deviation between the prediction result of the energy storage subsystem and the energy storage unit and the reference historical data is greater than the preset threshold, a fault warning is issued.
优选地，基于机器学习算法，构建储能总系统、储能子系统以及储能单元之间重要性的分析模型，还包括：基于机器学习算法，应用随机森林算法构建储能总系统、储能子系统以及储能单元之间重要性的分析模型。Preferably, based on a machine learning algorithm, an analysis model for the importance of the overall energy storage system, the energy storage subsystem, and the energy storage unit is constructed, which also includes: based on the machine learning algorithm, applying a random forest algorithm to construct the overall energy storage system, energy storage The analysis model of the importance among subsystems and energy storage units.
本申请根据重要性分析函数实时分析系统内各单元对总体运行参数的影响，根据重要性分析函数实时分析系统内各单元对总体运行参数的影响的过程为：This application analyzes the impact of each unit in the system on the overall operating parameters in real time according to the importance analysis function, and the process of analyzing the impact of each unit in the system on the overall operating parameters in real time according to the importance analysis function is:
本申请在采集储能系统数据的过程中，分别采集总体大规模储能系统运行过程中的状态参数，比如总体运行功率(Ptotal)、运行总电压(Vtotal)、总体荷电状态(SOCtotal)等，以及运行每一单体储能系统的运行功率(Pn)、运行电压(Vn)、荷电状态(SOCn)，以及下属各个单元的运行状态功率(Pnn)、运行电压(Vnn)、荷电状态(SOCn)，并将对应的储能总系统，储能子系统，相应的储能单元的同参数数据分类进而形成，储能系统运行功率数据库(DataP)、储能系统运行电压数据库(DataV)、储能系统运行荷电状态数据库(Datasoc)。In the process of collecting energy storage system data, the present application separately collects state parameters during the operation of the overall large-scale energy storage system, such as overall operating power (P total ), operating total voltage (V total ), overall state of charge (SOC total ), etc., as well as the operating power (P n ), operating voltage (Vn), and state of charge (SOCn) of each individual energy storage system, as well as the operating state power (Pnn) and operating voltage (V nn ), state of charge (SOC n ), and classify and form the same parameter data of the corresponding energy storage system, energy storage subsystem, and corresponding energy storage unit . Energy system operating voltage database (Data V ), energy storage system operating state of charge database (Data soc ).
本申请根据机器学习中的随机森林算法构造各系统运行参数针对于总系统的运行参数的重要性函数，同时构造各储能单元对其所属系统的运行参数重要性分析函数。形成重要性柱状图，分析各单元及各系统运行参数对整体系统的影响，以影响权重不低于80％为参考，选取相应的重要影响单元。According to the random forest algorithm in machine learning, this application constructs the importance function of the operating parameters of each system with respect to the operating parameters of the total system, and constructs an analysis function of the importance of each energy storage unit to the operating parameters of the system to which it belongs. Form an importance histogram to analyze the impact of each unit and each system operating parameter on the overall system, and select the corresponding important impact unit with the influence weight not less than 80% as a reference.
本申请根据重要性分析结果，采用时序预测方法对储能单元的运行状态进行时序预测分析，并提供预警信息。如图2所示，本申请采用时序预测方法对储能单元的运行状态进行时序预测分析，并提供预警信息的过程如下：According to the results of the importance analysis, this application adopts the time series prediction method to perform time series prediction analysis on the operation status of the energy storage unit, and provides early warning information. As shown in Figure 2, this application adopts the time-series prediction method to perform time-series prediction analysis on the operation status of the energy storage unit, and the process of providing early warning information is as follows:
建立储能总系统的运行状态参数时序曲线，建立储能子系统的运行状态参数时序曲线。同时根据重要性函数分析所得结果，建立重要影响单元与系统的状态参数时序对比分析图，对重要性影响的分析结果进行直观对比评价；The time series curve of the operating state parameters of the energy storage system is established, and the time series curve of the operating state parameters of the energy storage subsystem is established. At the same time, according to the results obtained from the analysis of the importance function, a time-series comparative analysis diagram of the state parameters of the important influencing units and the system is established, and the analysis results of the important influence are visually compared and evaluated;
根据重要性分析结果、系统及单元储能的历史数据，选用长短时预测神经网络对数据进行实时预测分析，本申请针对秒级采样数据，以1至2min的数据为输入数据，预测下一个时间单位的输出数据；According to the importance analysis results, the historical data of the system and unit energy storage, the long-short time prediction neural network is used to predict and analyze the data in real time. This application targets the second-level sampling data and uses the data of 1 to 2 minutes as the input data to predict the next time. the output data of the unit;
将预测曲线与真实运行情况进行实时对比分析，当系统出现较大波动时会导致时序预测出现较大偏差针对情况。根据相应的偏差幅度，在总体趋势可以达到实际预测需求的情况下，对系统预测结果进行相应的加权处理，当无法满足整体趋势时，修正网络结构并更新输入的训练数据集。同时设定根据实际工况设定预警标准，当预测结果1-2min之内全部低于标准则进行故障预警。Real-time comparison and analysis of the forecast curve and the real operation situation, when the system fluctuates greatly, it will cause a large deviation in the time series prediction. According to the corresponding deviation range, when the overall trend can meet the actual prediction requirements, the corresponding weighting process is carried out on the system prediction results. When the overall trend cannot be met, the network structure is corrected and the input training data set is updated. At the same time, the early warning standard is set according to the actual working conditions. When the predicted results are all lower than the standard within 1-2 minutes, a fault warning will be issued.
本申请实施方式提供一种规模化分布式电化学储能电站故障预警方法，是基于储能电池出厂额定参数及现场工况的运行状态参数等多种数据为基础，采用人工智能算法提取数据的重要性参数，并依据此选取所需跟踪预警的储能单元，考虑储能系统发电过程中某一储能单元发生故障时会对整体储能系统造成的影响。同时根据储能发电系统随时间呈现性能衰减的特性，采用时序分析方法对储能系统的运行状态参数进行时序预测，并根据实际运行情况及数据采集时间尺度确定一定时间范围内的预警值。通过这种故障预警方式可以在运行过程中提前预判可能发生故障的单元及对应的故障状态参数，通过预警机制保证储能系统安全平稳的运行。The implementation mode of this application provides a large-scale distributed electrochemical energy storage power station failure early warning method, which is based on various data such as the factory rated parameters of the energy storage battery and the operating state parameters of the on-site working conditions, and uses artificial intelligence algorithms to extract data. Importance parameters, and based on this, select the energy storage unit that needs to be tracked and warned, and consider the impact on the overall energy storage system when a certain energy storage unit fails during the power generation process of the energy storage system. At the same time, according to the characteristics of energy storage and power generation system showing performance decay over time, the time series analysis method is used to predict the operation state parameters of the energy storage system, and the early warning value within a certain time range is determined according to the actual operation situation and data collection time scale. Through this fault warning method, the units that may fail and the corresponding fault state parameters can be predicted in advance during the operation process, and the safe and stable operation of the energy storage system can be guaranteed through the early warning mechanism.
图3为根据本发明优选实施方式的规模化储能电站运行单元故障预警的系统结构图。如图3所示，一种规模化储能电站运行单元故障预警的系统，系统包括：Fig. 3 is a system structure diagram of a fault warning system for operating units of a large-scale energy storage power station according to a preferred embodiment of the present invention. As shown in Figure 3, a large-scale energy storage power station operation unit failure early warning system, the system includes:
第一采集单元301，用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将放电数据作为参考历史数据。优选地，第一采集单元用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，放电数据包括：实时放电功率、放电电压、以及荷电状态；当采集到的放电数据的数据量超过运行状态放电数据的额定数据量时，以实际工况的放电数据代替运行状态额定的放电数据作为新的运行状态放电数据；设置放电数据的预设数据量，当采集到的放电数据的数据量超过预设数据量时，则用新的放电数据代替历史放电数据。The first collection unit 301 is used to collect discharge data during the battery capacity decay process of the energy storage battery in a normal operating state, and use the discharge data as reference historical data. Preferably, the first collection unit is used to collect the discharge data during the battery capacity decay process under the normal operation state of the energy storage battery. The discharge data includes: real-time discharge power, discharge voltage, and state of charge; when the data of the collected discharge data When the amount exceeds the rated data volume of the discharge data in the running state, the discharge data of the actual working condition will replace the rated discharge data in the running state as the new discharge data in the running state; set the preset data volume of the discharge data, and when the collected discharge data When the amount of data exceeds the preset amount of data, the new discharge data is used to replace the historical discharge data.
第二采集单元302，用于基于大规模的储能系统，分别采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据。优选地，第二采集单元302用于基于大规模的储能系统，分别采集储能总系统、储能子系统以及储能子系统所包含的储能单元的运行状态参数数据包括：采集储能总系统的总体运行功率、运行总电压、以及总体荷电状态；采集储能子系统的运行功率、运行电压、以及荷电状态；采集储能单元的运行功率、运行电压、以及荷电状态；将储能总系统、储能子系统和储能单元的同类参数的数据进行分类，生成储能系统运行功率数据库、储能系统运行电压数据库和储能系统运行荷电状态数据库。The second collection unit 302 is configured to separately collect operating state parameter data of the total energy storage system, the energy storage subsystem, and the energy storage units included in the energy storage subsystem based on the large-scale energy storage system. Preferably, the second collection unit 302 is used to separately collect the operating state parameter data of the energy storage system, the energy storage subsystem, and the energy storage units contained in the energy storage subsystem based on a large-scale energy storage system, including: collecting energy storage Collect the overall operating power, operating voltage, and overall state of charge of the total system; collect the operating power, operating voltage, and state of charge of the energy storage subsystem; collect the operating power, operating voltage, and state of charge of the energy storage unit; Classify the data of similar parameters of the energy storage system, energy storage subsystem and energy storage unit to generate the energy storage system operating power database, energy storage system operating voltage database and energy storage system operating state of charge database.
优选地，第二采集单元302用于采集储能电池正常运行状态下电池容量衰减过程中的放电数据，将放电数据作为参考历史数据，还包括：电池容量按照不同速度衰减速度进行衰减。Preferably, the second collection unit 302 is used to collect the discharge data during the battery capacity decay process under the normal operation state of the energy storage battery, using the discharge data as reference historical data, and further includes: the battery capacity decays at different speeds.
构建单元303，用于基于机器学习算法，构建储能总系统、储能子系统以及储能单元之间重要性的分析模型；分析储能子系统与储能总系统的相关程度，通过判断每个储能子系统的运行状态参数在随机森林中的每棵树上对储能总系统的运行状态的影响，确定每个储能子系统的重要性因数；分析储能单元与储能子系统的相关程度，通过判断每个储能单元的运行状态参数在随机森林中的每棵树上对储能子系统的运行状态的影响，确定每个储能单元的重要性因数。优选地，构建单元303用于基于机器学习算法，构建储能总系统、储能子系统以及储能单元之间重要性的分析模型，还包括：基于机器学习算法，应用随机森林算法构建储能总系统、储能子系统以及储能单元之间重要性的分析模型。The construction unit 303 is used to construct an analysis model of the importance of the overall energy storage system, the energy storage subsystem, and the energy storage unit based on a machine learning algorithm; analyze the degree of correlation between the energy storage subsystem and the The impact of the operating state parameters of each energy storage subsystem on the operating state of the total energy storage system on each tree in the random forest, determine the importance factor of each energy storage subsystem; analyze the energy storage unit and energy storage subsystem By judging the impact of each energy storage unit’s operating state parameters on each tree in the random forest on the operating state of the energy storage subsystem, the importance factor of each energy storage unit is determined. Preferably, the construction unit 303 is used to construct an analysis model of the importance of the energy storage system, the energy storage subsystem, and the energy storage units based on a machine learning algorithm, and also includes: building an energy storage system based on a machine learning algorithm using a random forest algorithm The analysis model of the importance among the total system, the energy storage subsystem and the energy storage unit.
预警单元304，用于分别选取出储能子系统与储能单元的重要性因数大于预设阈值的储能子系统与储能单元，将储能子系统和储能单元的运行状态参数数据与参考历史数据采用长短时预测神经网络进行分析，获取下一时间单位的预测结果；当储能子系统和储能单元预测结果与参考历史数据的偏差大于预设的阈值时，发出故障预警。The early warning unit 304 is used to respectively select the energy storage subsystem and the energy storage unit whose importance factor is greater than the preset threshold, and compare the operating state parameter data of the energy storage subsystem and the energy storage unit with the The reference historical data is analyzed by the long-term and short-term prediction neural network to obtain the prediction result of the next time unit; when the deviation between the prediction result of the energy storage subsystem and the energy storage unit and the reference historical data is greater than the preset threshold, a fault warning is issued.
本发明优选实施方式的规模化储能电站运行单元故障预警的系统300与本发明优选实施方式的规模化储能电站运行单元故障预警的方法100相对应，在此不再进行赘述。The system 300 for early warning of faults of operating units of large-scale energy storage power plants in the preferred embodiment of the present invention corresponds to the method 100 for early warning of faults of operating units of large-scale energy storage power plants in the preferred embodiment of the present invention, and will not be repeated here.
已经通过参考少量实施方式描述了本发明。然而，本领域技术人员所公知的，正如附带的专利权利要求所限定的，除了本发明以上公开的其他的实施例等同地落在本发明的范围内。The invention has been described with reference to a small number of embodiments. However, it is clear to a person skilled in the art that other embodiments than the invention disclosed above are equally within the scope of the invention, as defined by the appended patent claims.
通常地，在权利要求中使用的所有术语都根据他们在技术领域的通常含义被解释，除非在其中被另外明确地定义。所有的参考“一个/所述/该[装置、组件等]”都被开放地解释为所述装置、组件等中的至少一个实例，除非另外明确地说明。这里公开的任何方法的步骤都没必要以公开的准确的顺序运行，除非明确地说明。Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a/the/the [means, component, etc.]" are openly construed to mean at least one instance of said means, component, etc., unless expressly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111126808A (en) * | 2019-12-11 | 2020-05-08 | 天津泰凡科技有限公司 | Data early warning method and device and computer storage medium |
| CN111159224A (en) * | 2020-01-03 | 2020-05-15 | 珠海格力电器股份有限公司 | Energy storage system control method and device and park level energy storage system |
| CN113655391A (en) * | 2021-08-26 | 2021-11-16 | 江苏慧智能源工程技术创新研究院有限公司 | Energy storage power station battery fault diagnosis method based on LightGBM model |
| CN115902646A (en) * | 2023-01-06 | 2023-04-04 | 中国电力科学研究院有限公司 | Energy storage battery fault identification method and system |
| CN115963408A (en) * | 2022-12-19 | 2023-04-14 | 北京双登慧峰聚能科技有限公司 | Fault early warning system and method for single battery of energy storage power station |
| CN116577673A (en) * | 2023-07-12 | 2023-08-11 | 深圳先进储能材料国家工程研究中心有限公司 | Distributed neural network-based energy storage power station fault diagnosis method and system |
| CN117079435A (en) * | 2023-08-25 | 2023-11-17 | 中国能源建设股份有限公司 | Early warning method and system for oil tank of compressed air energy storage power station compressor |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104638772A (en) * | 2013-11-14 | 2015-05-20 | 国家电网公司 | Battery energy storage power station energy management method based on wind power prediction |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| CN107942255A (en) * | 2017-11-03 | 2018-04-20 | 山东智洋电气股份有限公司 | A kind of storage batteries of transformer substation group state evaluating method based on Data fusion technique |
| CN108445410A (en) * | 2018-04-02 | 2018-08-24 | 国家计算机网络与信息安全管理中心 | A kind of method and device of monitoring accumulator group operating status |
| CN108551167A (en) * | 2018-04-25 | 2018-09-18 | 浙江大学 | A kind of electric power system transient stability method of discrimination based on XGBoost algorithms |
| CN108808671A (en) * | 2018-07-03 | 2018-11-13 | 南京信息工程大学 | A kind of short-term wind speed DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM method of wind power plant |
| CN108986470A (en) * | 2018-08-20 | 2018-12-11 | 华南理工大学 | The Travel Time Estimation Method of particle swarm algorithm optimization LSTM neural network |
| CN109143085A (en) * | 2018-07-18 | 2019-01-04 | 中国电力科学研究院有限公司 | A kind of method and system carrying out early warning to lithium ion battery based on intelligent algorithm |
| CN109560577A (en) * | 2018-12-04 | 2019-04-02 | 东莞理工学院 | A kind of control method and system of alternating current-direct current mixed distribution formula renewable energy system |
| CN109681391A (en) * | 2017-10-18 | 2019-04-26 | 中车株洲电力机车研究所有限公司 | A kind of blade root bolt fracture fault detection method and medium |
| CN109755949A (en) * | 2019-01-07 | 2019-05-14 | 中国电力科学研究院有限公司 | A method and device for optimal distribution of power in a combined heat and power energy storage power station |
-
2019
- 2019-07-25 CN CN201910676678.4A patent/CN110535159B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104638772A (en) * | 2013-11-14 | 2015-05-20 | 国家电网公司 | Battery energy storage power station energy management method based on wind power prediction |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| CN109681391A (en) * | 2017-10-18 | 2019-04-26 | 中车株洲电力机车研究所有限公司 | A kind of blade root bolt fracture fault detection method and medium |
| CN107942255A (en) * | 2017-11-03 | 2018-04-20 | 山东智洋电气股份有限公司 | A kind of storage batteries of transformer substation group state evaluating method based on Data fusion technique |
| CN108445410A (en) * | 2018-04-02 | 2018-08-24 | 国家计算机网络与信息安全管理中心 | A kind of method and device of monitoring accumulator group operating status |
| CN108551167A (en) * | 2018-04-25 | 2018-09-18 | 浙江大学 | A kind of electric power system transient stability method of discrimination based on XGBoost algorithms |
| CN108808671A (en) * | 2018-07-03 | 2018-11-13 | 南京信息工程大学 | A kind of short-term wind speed DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM method of wind power plant |
| CN109143085A (en) * | 2018-07-18 | 2019-01-04 | 中国电力科学研究院有限公司 | A kind of method and system carrying out early warning to lithium ion battery based on intelligent algorithm |
| CN108986470A (en) * | 2018-08-20 | 2018-12-11 | 华南理工大学 | The Travel Time Estimation Method of particle swarm algorithm optimization LSTM neural network |
| CN109560577A (en) * | 2018-12-04 | 2019-04-02 | 东莞理工学院 | A kind of control method and system of alternating current-direct current mixed distribution formula renewable energy system |
| CN109755949A (en) * | 2019-01-07 | 2019-05-14 | 中国电力科学研究院有限公司 | A method and device for optimal distribution of power in a combined heat and power energy storage power station |
Non-Patent Citations (5)
| Title |
|---|
| HARISH ANANTHARAMAN: "Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification", 《2018 IEEE CONFERENCE ON E-LEARNING, E-MANAGEMENT AND E-SERVICES (IC3E)》 * |
| M. KROENER等: "Characterization of energy storage components for a versatile energy management system", 《ENERGY SELF-SUFFICIENT SENSORS 7TH GMM-WORKSHOP》 * |
| 李俊卿等: "基于随机森林重要性的LSTM网络风电功率缺失数据补齐", 《微电网技术》 * |
| 蒋卫祥等: "《大数据时代计算机数据处理技术探究》", 31 March 2019 * |
| 陆继翔等: "基于CNN—LSTM混合神经网络模型的短期负荷预测方法", 《电力系统自动化》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111126808A (en) * | 2019-12-11 | 2020-05-08 | 天津泰凡科技有限公司 | Data early warning method and device and computer storage medium |
| CN111159224A (en) * | 2020-01-03 | 2020-05-15 | 珠海格力电器股份有限公司 | Energy storage system control method and device and park level energy storage system |
| CN113655391A (en) * | 2021-08-26 | 2021-11-16 | 江苏慧智能源工程技术创新研究院有限公司 | Energy storage power station battery fault diagnosis method based on LightGBM model |
| CN115963408A (en) * | 2022-12-19 | 2023-04-14 | 北京双登慧峰聚能科技有限公司 | Fault early warning system and method for single battery of energy storage power station |
| CN115963408B (en) * | 2022-12-19 | 2024-04-16 | 北京双登慧峰聚能科技有限公司 | Energy storage power station single battery fault early warning system and method |
| CN115902646A (en) * | 2023-01-06 | 2023-04-04 | 中国电力科学研究院有限公司 | Energy storage battery fault identification method and system |
| CN115902646B (en) * | 2023-01-06 | 2023-06-13 | 中国电力科学研究院有限公司 | Energy storage battery fault identification method and system |
| CN116577673A (en) * | 2023-07-12 | 2023-08-11 | 深圳先进储能材料国家工程研究中心有限公司 | Distributed neural network-based energy storage power station fault diagnosis method and system |
| CN116577673B (en) * | 2023-07-12 | 2023-09-12 | 深圳先进储能材料国家工程研究中心有限公司 | Distributed neural network-based energy storage power station fault diagnosis method and system |
| CN117079435A (en) * | 2023-08-25 | 2023-11-17 | 中国能源建设股份有限公司 | Early warning method and system for oil tank of compressed air energy storage power station compressor |
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